The largest reported data leakage as of January 2025 was the Cam4 data breach in March 2020, which exposed more than 10 billion data records. The second-largest data breach in history so far, the Yahoo data breach, occurred in 2013. The company initially reported about one billion exposed data records, but after an investigation, the company updated the number, revealing that three billion accounts were affected. The National Public Data Breach was announced in August 2024. The incident became public when personally identifiable information of individuals became available for sale on the dark web. Overall, the security professionals estimate the leakage of nearly three billion personal records. The next significant data leakage was the March 2018 security breach of India's national ID database, Aadhaar, with over 1.1 billion records exposed. This included biometric information such as identification numbers and fingerprint scans, which could be used to open bank accounts and receive financial aid, among other government services.

Cybercrime - the dark side of digitalization As the world continues its journey into the digital age, corporations and governments across the globe have been increasing their reliance on technology to collect, analyze and store personal data. This, in turn, has led to a rise in the number of cyber crimes, ranging from minor breaches to global-scale attacks impacting billions of users – such as in the case of Yahoo. Within the U.S. alone, 1802 cases of data compromise were reported in 2022. This was a marked increase from the 447 cases reported a decade prior. The high price of data protection As of 2022, the average cost of a single data breach across all industries worldwide stood at around 4.35 million U.S. dollars. This was found to be most costly in the healthcare sector, with each leak reported to have cost the affected party a hefty 10.1 million U.S. dollars. The financial segment followed closely behind. Here, each breach resulted in a loss of approximately 6 million U.S. dollars - 1.5 million more than the global average.

Version 3 with 517M hashes and counts of password usage ordered by most to least prevalent Pwned Passwords are 517,238,891 real world passwords previously exposed in data breaches. This exposure makes them unsuitable for ongoing use as they re at much greater risk of being used to take over other accounts. They re searchable online below as well as being downloadable for use in other online system. The entire set of passwords is downloadable for free below with each password being represented as a SHA-1 hash to protect the original value (some passwords contain personally identifiable information) followed by a count of how many times that password had been seen in the source data breaches. The list may be integrated into other systems and used to verify whether a password has previously appeared in a data breach after which a system may warn the user or even block the password outright.

During the third quarter of 2024, data breaches exposed more than *** million records worldwide. Since the first quarter of 2020, the highest number of data records were exposed in the first quarter of ***, more than *** million data sets. Data breaches remain among the biggest concerns of company leaders worldwide. The most common causes of sensitive information loss were operating system vulnerabilities on endpoint devices. Which industries see the most data breaches? Meanwhile, certain conditions make some industry sectors more prone to data breaches than others. According to the latest observations, the public administration experienced the highest number of data breaches between 2021 and 2022. The industry saw *** reported data breach incidents with confirmed data loss. The second were financial institutions, with *** data breach cases, followed by healthcare providers. Data breach cost Data breach incidents have various consequences, the most common impact being financial losses and business disruptions. As of 2023, the average data breach cost across businesses worldwide was **** million U.S. dollars. Meanwhile, a leaked data record cost about *** U.S. dollars. The United States saw the highest average breach cost globally, at **** million U.S. dollars.

In 2024, the number of data compromises in the United States stood at 3,158 cases. Meanwhile, over 1.35 billion individuals were affected in the same year by data compromises, including data breaches, leakage, and exposure. While these are three different events, they have one thing in common. As a result of all three incidents, the sensitive data is accessed by an unauthorized threat actor. Industries most vulnerable to data breaches Some industry sectors usually see more significant cases of private data violations than others. This is determined by the type and volume of the personal information organizations of these sectors store. In 2024 the financial services, healthcare, and professional services were the three industry sectors that recorded most data breaches. Overall, the number of healthcare data breaches in some industry sectors in the United States has gradually increased within the past few years. However, some sectors saw decrease. Largest data exposures worldwide In 2020, an adult streaming website, CAM4, experienced a leakage of nearly 11 billion records. This, by far, is the most extensive reported data leakage. This case, though, is unique because cyber security researchers found the vulnerability before the cyber criminals. The second-largest data breach is the Yahoo data breach, dating back to 2013. The company first reported about one billion exposed records, then later, in 2017, came up with an updated number of leaked records, which was three billion. In March 2018, the third biggest data breach happened, involving India’s national identification database Aadhaar. As a result of this incident, over 1.1 billion records were exposed.

This is the first fully labeled open dataset for leak detection and localization in water distribution systems. This dataset includes two hundred and eighty signals acquired from a laboratory-scale water distribution testbed with four types of induced leaks and no-leak. The testbed was 47 m long built from 152.4 mm diameter PVC pipes. Two accelerometers (A1 and A2), two hydrophones (H1 and H2), and two dynamic pressure sensors (P1 and P2) were deployed to measure acceleration, acoustic, and dynamic pressure data. The data were recorded through controlled experiments where the following were changed: network architecture, leak type, background flow condition, background noise condition, and sensor types and locations. Each signal was recorded for 30 seconds. Network architectures were looped (LO) and branched (BR). Leak types were Longitudinal Crack (LC), Circumferential Crack (CC), Gasket Leak (GL), Orifice Leak (OL), and No-leak (NL). Background flow conditions included 0 L/s (ND), 0.18 L/s, 0.47 L/s, and Transient (background flow rate abruptly changed from 0.47 L/s to 0 L/s at the second 20th of 30-second long measurements). Background noise conditions, with noise (N) and without noise (NN), determined whether a background noise was present during acoustic data measurements. Accelerometer and dynamic pressure data are in ‘.csv’ format, and the hydrophone data are in ‘.raw’ format with 8000 Hz frequency. The file “Python code to convert raw acoustic data to pandas DataFrame.py” converts the raw hydrophone data to DataFrame in Python.

The list contains every wordlist, dictionary, and password database leak that I could find on the internet (and I spent a LOT of time looking). It also contains every word in the Wikipedia databases (pages-articles, retrieved 2010, all languages) as well as lots of books from Project Gutenberg. It also includes the passwords from some low-profile database breaches that were being sold in the underground years ago. The format of the list is a standard text file sorted in non-case-sensitive alphabetical order. Lines are separated with a newline " " character. You can test the list without downloading it by giving SHA256 hashes to the free hash cracker or to @PlzCrack on twitter. Here s a tool for computing hashes easily. Here are the results of cracking LinkedIn s and eHarmony s password hash leaks with the list. The list is responsible for cracking about 30% of all hashes given to CrackStation s free hash cracker, but that figure should be taken with a grain of salt because s

500k User Session Collection This collection is distributed for NON-COMMERCIAL RESEARCH USE ONLY. Any application of this collection for commercial purposes is STRICTLY PROHIBITED. #### Brief description: This collection consists of ~20M web queries collected from ~650k users over three months. The data is sorted by anonymous user ID and sequentially arranged. The goal of this collection is to provide real query log data that is based on real users. It could be used for personalization, query reformulation or other types of search research. The data set includes AnonID, Query, QueryTime, ItemRank, ClickURL. AnonID - an anonymous user ID number. Query - the query issued by the user, case shifted with most punctuation removed. QueryTime - the time at which the query was submitted for search. ItemRank - if the user clicked on a search result, the rank of the item on which they clicked is listed. ClickURL - if the user clicked on a search result, the domain portion of the URL i

As of 2024, the average cost of a data breach in the United States amounted to 9.36 million U.S. dollars, down from 9.48 million U.S. dollars in the previous year. The global average cost per data breach was 4.88 million U.S. dollars in 2024. Cost of a data breach in different countries worldwide Data breaches impose a big threat for organizations globally. The monetary damage caused by data breaches has increased in many markets in the past decade. In 2023, Canada followed the U.S. by data breach costs, with an average of 5.13 million U.S. dollars. Since 2019, the average monetary damage caused by loss of sensitive information in Canada has increased notably. In the United Kingdom, the average cost of a data breach in 2024 amounted to around 4.53 million U.S. dollars, while in Germany it stood at 5.31 million U.S. dollars. The cost of data breach by industry and segment Data breach costs vary depending on the industry and segment. For the fourth consecutive year, the global healthcare sector registered the highest costs of data breach, which in 2024 amounted to about nine million U.S. dollars. Financial institutions ranked second, with an average cost of six million U.S. dollars for a data breach. Detection and escalation was the costliest segment in data breaches worldwide, with 1.63 U.S. dollars on average. The cost for lost business ranked second, while response following a breach came across as the third-costliest segment.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The data center water leak detector market is experiencing robust growth, driven by the increasing adoption of data centers globally and the rising awareness of the significant financial and operational losses associated with water damage. The market, estimated at $500 million in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key factors: the escalating demand for high-availability and uptime in data centers, stricter regulatory compliance requirements regarding data center safety, and the continuous advancement of leak detection technologies offering greater precision and faster response times. The market is segmented by application (commercial, industrial, other) and type (non-positioned and positioned leak detection). The positioned water leak detection segment is expected to dominate due to its ability to pinpoint leaks precisely, minimizing downtime and repair costs. North America and Europe currently hold the largest market share, driven by high data center density and strong regulatory frameworks. However, the Asia-Pacific region is poised for significant growth, fueled by rapid data center construction in countries like China and India. Market restraints include the high initial investment cost of deploying advanced leak detection systems, particularly in smaller data centers. However, the long-term cost savings associated with preventing catastrophic water damage significantly outweigh this initial investment. Furthermore, the increasing availability of cloud-based monitoring and remote management solutions is further driving adoption by streamlining maintenance and reducing operational overhead. Leading companies in this market are actively innovating to enhance the functionality and cost-effectiveness of their products, including integrating advanced analytics and AI for predictive maintenance and proactive leak prevention. The competitive landscape is characterized by a mix of established players and emerging technology providers, driving innovation and creating opportunities for market consolidation in the coming years.

Full title: Using Decision Trees to Detect and Isolate Simulated Leaks in the J-2X Rocket Engine Mark Schwabacher, NASA Ames Research Center Robert Aguilar, Pratt & Whitney Rocketdyne Fernando Figueroa, NASA Stennis Space Center Abstract The goal of this work was to use data-driven methods to automatically detect and isolate faults in the J-2X rocket engine. It was decided to use decision trees, since they tend to be easier to interpret than other data-driven methods. The decision tree algorithm automatically “learns” a decision tree by performing a search through the space of possible decision trees to find one that fits the training data. The particular decision tree algorithm used is known as C4.5. Simulated J-2X data from a high-fidelity simulator developed at Pratt & Whitney Rocketdyne and known as the Detailed Real-Time Model (DRTM) was used to “train” and test the decision tree. Fifty-six DRTM simulations were performed for this purpose, with different leak sizes, different leak locations, and different times of leak onset. To make the simulations as realistic as possible, they included simulated sensor noise, and included a gradual degradation in both fuel and oxidizer turbine efficiency. A decision tree was trained using 11 of these simulations, and tested using the remaining 45 simulations. In the training phase, the C4.5 algorithm was provided with labeled examples of data from nominal operation and data including leaks in each leak location. From the data, it “learned” a decision tree that can classify unseen data as having no leak or having a leak in one of the five leak locations. In the test phase, the decision tree produced very low false alarm rates and low missed detection rates on the unseen data. It had very good fault isolation rates for three of the five simulated leak locations, but it tended to confuse the remaining two locations, perhaps because a large leak at one of these two locations can look very similar to a small leak at the other location. Introduction The J-2X rocket engine will be tested on Test Stand A-1 at NASA Stennis Space Center (SSC) in Mississippi. A team including people from SSC, NASA Ames Research Center (ARC), and Pratt & Whitney Rocketdyne (PWR) is developing a prototype end-to-end integrated systems health management (ISHM) system that will be used to monitor the test stand and the engine while the engine is on the test stand[1]. The prototype will use several different methods for detecting and diagnosing faults in the test stand and the engine, including rule-based, model-based, and data-driven approaches. SSC is currently using the G2 tool http://www.gensym.com to develop rule-based and model-based fault detection and diagnosis capabilities for the A-1 test stand. This paper describes preliminary results in applying the data-driven approach to detecting and diagnosing faults in the J-2X engine. The conventional approach to detecting and diagnosing faults in complex engineered systems such as rocket engines and test stands is to use large numbers of human experts. Test controllers watch the data in near-real time during each engine test. Engineers study the data after each test. These experts are aided by limit checks that signal when a particular variable goes outside of a predetermined range. The conventional approach is very labor intensive. Also, humans may not be able to recognize faults that involve the relationships among large numbers of variables. Further, some potential faults could happen too quickly for humans to detect them and react before they become catastrophic. Automated fault detection and diagnosis is therefore needed. One approach to automation is to encode human knowledge into rules or models. Another approach is use data-driven methods to automatically learn models from historical data or simulated data. Our prototype will combine the data-driven approach with the model-based and rule-based appro

Water Leak Detection Sensors Market Outlook

The global water leak detection sensors market size was valued at $1.5 billion in 2023 and is projected to reach $2.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.1% from 2024 to 2032. The rapid growth of this market can be attributed to increasing urbanization, the need for efficient water management, and rising awareness about water conservation.

One of the primary growth factors in the water leak detection sensors market is the increasing scarcity of water resources globally. With growing populations and expanding urban areas, the demand for water is skyrocketing, necessitating advanced systems for its effective management. Water leak detection sensors are becoming essential tools in urban planning and infrastructure development, helping to minimize wastage and ensuring sustainable water supply systems. Governments and environmental organizations are actively promoting innovations in water management technologies, further accelerating the market growth.

Another significant driver is technological advancement in sensor technology. Innovations such as the Internet of Things (IoT) have revolutionized the water leak detection landscape. IoT-enabled sensors provide real-time data and remote monitoring capabilities, allowing for instantaneous leak detection and reducing response times significantly. These advanced solutions are not only more efficient but also more cost-effective in the long term, driving their adoption across various sectors including residential, commercial, and industrial applications.

Furthermore, stringent regulatory frameworks and policies aimed at water conservation and management are propelling the adoption of water leak detection systems. Governments worldwide are implementing regulations that mandate the use of efficient water management systems to curb wastage. For instance, regions prone to water scarcity issues, such as the Middle East & Africa and parts of Asia Pacific, have stringent policies that enforce the usage of advanced leak detection systems, thereby driving market growth. These regulatory measures are not only limited to water utilities but extend to other sectors like oil & gas, manufacturing, and data centers, contributing to the market expansion.

The integration of Smart Home Water Sensor and Controller systems is revolutionizing the way homeowners manage water usage and detect leaks. These systems not only provide real-time alerts but also allow users to remotely control water flow, significantly reducing the risk of water damage. By leveraging IoT technology, smart home water sensors can communicate with other devices in the home, creating a seamless and efficient water management system. This not only enhances convenience but also contributes to water conservation efforts by allowing users to monitor and adjust their water usage patterns. As more consumers adopt smart home technologies, the demand for integrated water management solutions is expected to rise, further driving the growth of the water leak detection sensors market.

From a regional perspective, North America and Europe are expected to be leading markets for water leak detection sensors, driven by advanced infrastructure and high levels of awareness regarding water conservation. However, Asia Pacific is anticipated to exhibit the highest growth rate during the forecast period, owing to rapid urbanization and significant investments in smart city projects. Emerging economies in Latin America and the Middle East & Africa are also expected to witness substantial growth, supported by increasing government initiatives focusing on water management and infrastructural improvements.

Product Type Analysis

In the realm of product types, acoustic sensors have gained significant traction due to their high accuracy and reliability in detecting leaks. These sensors work by picking up sound waves generated by leaks and are particularly effective in detecting small leaks that might not be immediately visible. Acoustic sensors are widely used in both residential and commercial applications due to their efficiency in pinpointing the exact location of leaks, minimizing damage, and reducing repair costs. Their ability to integrate seamlessly with IoT-based systems further enhances their appeal, making them a popular choice in advanced water management solutions.

Pressure sensors represent another critical segment in the wa

Oil and Gas Pipeline Leak Detection Market Outlook

The global oil and gas pipeline leak detection market size is projected to experience significant growth, with an expected valuation rising from USD 2.37 billion in 2023 to USD 3.89 billion by 2032, reflecting a healthy compound annual growth rate (CAGR) of 5.6% from 2024 to 2032. This market expansion is largely fueled by the increasing emphasis on safety and environmental regulations, the growing complexity of pipeline networks, and the dire need for efficient and reliable leak detection systems. As governments and organizations worldwide become more aware of and committed to reducing the environmental impacts of fossil fuel extraction and transportation, the demand for advanced leak detection technologies has intensified, driving market growth.

One of the primary factors contributing to the growth of the oil and gas pipeline leak detection market is the stringent regulatory frameworks being implemented globally to prevent environmental disasters. These regulations mandate the installation of sophisticated leak detection systems to minimize the risk of oil spills and gas leaks, which can have catastrophic environmental and economic consequences. The increasing public awareness and pressure on governments to ensure the safety and integrity of oil and gas infrastructure have also played a crucial role in driving the market's expansion. Furthermore, the adoption of best practices and international standards in pipeline monitoring and maintenance is further propelling the demand for innovative and reliable leak detection technologies.

Technological advancements in the oil and gas industry have paved the way for the development of more efficient and accurate leak detection systems. Innovations such as acoustic/ultrasonic sensors, fiber optic technologies, and advanced data analytics are improving the precision and reliability of leak detection, thereby reducing operational risks and potential losses. The integration of Internet of Things (IoT) and artificial intelligence (AI) in pipeline monitoring systems enhances real-time data collection and analysis, enabling prompt detection and response to leaks. These cutting-edge technologies are not only enhancing the effectiveness of leak detection but also reducing the overall costs associated with pipeline monitoring and maintenance, making them increasingly attractive to oil and gas companies.

The growing global energy demand and the expansion of oil and gas pipeline networks, especially in emerging economies, are also driving the need for efficient leak detection systems. As countries endeavor to secure their energy supply and improve infrastructure, significant investments are being made in the construction and maintenance of extensive pipeline networks. This expansion necessitates robust leak detection solutions to ensure the safe and efficient transportation of oil and gas resources. Additionally, the shift towards unconventional oil and gas resources, such as shale gas and deepwater drilling, presents new challenges in leak detection, further increasing the demand for advanced technologies.

Pipeline Leak Detectors play a crucial role in ensuring the safety and efficiency of oil and gas transportation. These detectors are designed to identify leaks quickly and accurately, minimizing the risk of environmental damage and economic loss. By utilizing advanced technologies such as acoustic sensors and fiber optics, pipeline leak detectors can provide real-time monitoring and immediate alerts, allowing operators to respond swiftly to any potential issues. This capability is particularly important in complex pipeline networks, where undetected leaks can lead to significant operational challenges. As the industry continues to evolve, the integration of pipeline leak detectors with digital technologies like AI and IoT is enhancing their effectiveness, offering more precise detection and predictive maintenance capabilities.

Technology Analysis

The technology segment of the oil and gas pipeline leak detection market encompasses various sophisticated systems, each offering unique advantages in detecting leaks with precision. Acoustic/ultrasonic technology, for instance, stands out for its ability to detect leaks through sound waves. This method is particularly effective in situations where traditional methods may fall short, as it can monitor for changes in noise levels along pipeline routes, indicating potential leaks. The sensitivity of acoustic/ultrasonic systems to sound variations makes th

1848 Global import shipment records of Leak Detection with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Passwords that were leaked or stolen from sites. The Rockyou Dataset is about 14 million passwords.

The global data center water leak detector market is experiencing robust growth, driven by the increasing prevalence of data centers worldwide and the rising awareness of the substantial financial and operational losses associated with water damage. The market, valued at $145.2 million in 2025, is projected to exhibit significant expansion over the forecast period (2025-2033). While the provided CAGR is missing, a reasonable estimate, considering the growth drivers and technological advancements in leak detection systems, would place the CAGR between 8% and 12% annually. This growth is fueled by several factors: the burgeoning demand for cloud computing and digital services necessitates more data centers, making them more vulnerable to water damage; the increasing adoption of advanced detection technologies like non-positioned and positioned sensors providing earlier and more precise alerts; and stringent regulations emphasizing data center resilience and uptime. Furthermore, the market is segmented by application (commercial, industrial, public buildings, and dedicated data centers) and type of detection technology (non-positioned and positioned systems). Competition is intense, with established players like Honeywell and Siemens alongside specialized firms like TATSUTA and TTK Leak Detection vying for market share through innovation and strategic partnerships. The geographical distribution of the market is diverse, with North America and Europe currently holding significant shares. However, the Asia-Pacific region is expected to witness the fastest growth due to the rapid expansion of data centers in countries like China and India. Challenges remain, including the high initial investment costs of installing advanced detection systems and the need for ongoing maintenance. However, the significant long-term cost savings associated with preventing water damage are likely to outweigh these concerns, further fueling market expansion. The market’s future trajectory is positive, underpinned by the relentless growth of the data center industry and continuous innovations in leak detection technologies. The integration of IoT and AI capabilities is expected to further enhance the efficiency and effectiveness of these systems, driving market growth in the coming years.

DroidLeaks features 292 diverse resource leak bugs in popular and large-scale open-source Android apps. For each bug, DroidLeaks provides links to:
1. the code repository of the app subject
2. the concerned resource class
3. the buggy code revision (and buggy file and method names)
4. the bug-fixing code revision (i.e., link to the patch)
5. the bug report or the corresponding pull request for patches (if located)

Water leaks data set.

The global fuel oil leak detection systems market is experiencing robust growth, driven by stringent environmental regulations aimed at minimizing oil spills and their devastating consequences. The increasing adoption of advanced sensing technologies, coupled with rising awareness of operational safety and the need to prevent costly environmental remediation efforts, are key market drivers. The market is segmented by application (oil depots, pipelines, airports, refineries, and others) and type (sensors, sensor cables, and others). Pipelines and refineries currently represent the largest application segments, given their susceptibility to leaks and the significant environmental and financial risks associated with them. However, increasing air travel and the expansion of airport infrastructure are creating substantial growth opportunities in the airport segment. Technological advancements, such as the integration of IoT and AI for enhanced leak detection and predictive maintenance, are transforming the market landscape, enabling more accurate and timely responses to potential leaks. Furthermore, the development of sophisticated sensor technologies with improved sensitivity and reliability is contributing to market expansion. The competitive landscape is characterized by a mix of established players and emerging technology providers, fostering innovation and driving down costs. While the initial investment in these systems can be substantial, the long-term cost savings associated with preventing catastrophic leaks and regulatory fines are incentivizing adoption across various sectors. Geographic growth is largely concentrated in regions with extensive oil and gas infrastructure and robust environmental regulations, with North America and Europe currently leading the market. However, rapid industrialization in Asia-Pacific is fueling significant growth potential in this region in the coming years. The market's Compound Annual Growth Rate (CAGR) indicates a steady upward trajectory. This growth is anticipated to continue throughout the forecast period, largely due to ongoing investments in infrastructure modernization and the increasing focus on sustainable practices within the energy sector. While challenges remain – such as the high initial cost of installation and the potential for false positives – the overall market outlook for fuel oil leak detection systems remains positive, suggesting continued expansion in the coming decade. Furthermore, government initiatives promoting environmental protection and safety standards are further bolstering market growth. The market is expected to see further diversification with new technologies and players entering the scene, leading to increased competition and innovation.

312 Global export shipment records of Leak Detection System with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.